The present invention relates in general to the field of electronic systems and, more particularly, to the Self-Tuning Control method and system of a switched-reluctance motor drive.
Due to the simple motor construction and power converter requirements, switched-reluctance motor (xe2x80x9cSRMxe2x80x9d) drives have been found competitive with traditional ac and dc drives. However, the control of the SRM is not yet a perfected art. The controllable variables of the SRM are the phase current magnitude (I), the turn-on angle (xcex1), and the turn-off command angle (xcex2) The control problem may be broadly divided into the following two questions.
(a) How does one determine the instants of time at which the motor reaches the desired switching angles of xcex1 and xcex2?
(b) What are the optimal values of xcex1, xcex2, I which satisfy the speed and torque demanded by the drive?
Problem (a) is usually solved by a shaft position sensor. This patent application addresses problem (b), assuming that position feedback data is available from a shaft sensor. The optimal performance of the drive may be described by different figures of merit, such as drive efficiency, torque per ampere (xe2x80x9cTPAxe2x80x9d), and torque ripple. Prior art publications describe the use of extensive computer simulations to demonstrate the possibility of obtaining maximum drive efficiency by means of controlling the firing angles, describe a use of optimal current waveforms for maximum torque per RMS ampere and analytically analyze the firing angle control for a maximum torque. The control strategies are usually based on a knowledge of the motor inductance profiles as a function of position. A reduction of torque ripple can also be achieved if this data is utilized. Conventionally, only simple control strategies have been implemented on actual SRM drives, such as a linear advance of the firing angles with increasing speed and load or a minimization of the conduction angle. There also has been an attempt to optimize drive efficiency on-line by varying the firing angles.
It has been realized that due to parameter variation and drift, the phase inductance profiles can significantly differ from the design data. Therefore, it may be preferable to use a controller with self-tuning capability if an optimal performance of the SRM drive is to be maintained.
The inherent simplicity, ruggedness, and low cost of a SRM make it a potential candidate for various general purpose adjustable speed applications.
However, a comprehensive mathematical modeling of SRM is extremely complicated due to its highly non-linear characteristics arising from its saturation region of operation. Several attempts have been made to derive a nonlinear mathematical model of the SRM. Attempts on linearization of the complicated nonlinear SRM model have also been made.
However, these conventional methods suffer from several limitations. For example, they use complex modeling and computation time, and they lack accuracy. The capability to accommodate accurate non-linear modeling has made Artificial Neural Networks (xe2x80x9cANNsxe2x80x9d) an ideal candidate to solve the control strategies of an inherently nonlinear system. A self organized Kohonen neural network has been previously provided for the modeling of nonlinear SRM torque characteristics as a function of position and current. A nonlinear modeling of SRM based on the back propagation neural network has also been discussed, as well as a torque ripple minimized control of SRM using ANNs. In all these prior art approaches, the neural net training is performed using static magnetization data which is generated experimentally. Although these neural networks trained with the static test data may perform well for the steady state operation, they fail to function adequately in the dynamic regime.
The main advantage of SRM is in its simple yet rugged rotor construction. Though it has a simple structure, its control is very complicated due to the highly nonlinear characteristics of the machine. The advances made in the field of Digital Signal Processors (xe2x80x9cDSPsxe2x80x9d) can be utilized in developing advanced digital controllers which can handle complicated control strategies. Several control strategies have been developed to improve the performance of the SRM drives. The performance indices usually considered were maximum torque, torque ripple and drive efficiency. Prior publications on the optimization of the SRM drive performance consisted mainly of off-line calculations to find the excitation instances to optimize performance indices like efficiency and torque output. These control strategies are based on the assumption that there can be no occurrence of parameter variations that may change the electrical characteristics of the machine. However, significant SRM parameter variations occur in its mass production or with motor aging. Control techniques with self-tuning capability are essential to maintain an optimal performance of the SRM drive, in the presence of parameter variations. Prior publications indicate that parameter variations can alter the inductance profile to a significant extent. Since the control of the SRM is essentially based on the inductance profile, it necessitates an on-line self-tuning control strategy for optimum performance. Some work for an on-line optimization of the SRM drive efficiency and torque per ampere has been conducted. The prior art publications describe a self-tuning control method which takes into account the variations in the inductance profile due to parameter variations. But this conventional method only has a finite accuracy, a restricted self-learning capability and a limited dynamic response. Neural Networks (xe2x80x9cNNsxe2x80x9d) have been successfully used for many applications in control systems. But the NN learning algorithm performs remarkably well when used off-line, i.e., it has to be fully trained before being applied. NNs with incremental learning capability with stable adaptation of network parameters are preferable for on-line adaptive control. Foslien et al. suggested a simple, model-independent method which is based on the assumption that the NN to start with is well trained in such a way that it can perform input/output mapping for the initial training set with high degree of accuracy. This can be achieved by training the NN with sufficient amount of data to a very low error rate. According to an exemplary embodiment of the present invention, this training can be done off-line as it may require more time. The present invention provides an improved control technique that overcomes the above-described shortcomings and optimizes the drive performance as measured by torque per ampere. With reference to the discussion provided below, torque per ampere is defined as the ratio of the average torque to the phase current amplitude.
In accordance with the present invention, a switched-reluctance motor drive system is provided that substantially reduces or eliminates problems associated with the prior systems.
One of the embodiments of the system and method according to the present invention solves the problem of obtaining optimal performance from the SRM motor in the presence of a parameter variation, which can alter the phase inductance profiles. The operation of an SRM drive in the controlled current mode from zero to base speed can be considered. A shaft position sensor may be utilized for commutation of the power converter. Self-tuning methods according to the present invention which optimize the steady-state performance of the drive as measured by TPA can also be utilized. Computer simulations can also be employed to show the existence and uniqueness of a solution to the optimal TPA problem.
In another embodiment of the present invention, the drive performance is optimized as measured by torque per ampere. One exemplary variant of the method according to the present invention utilizes the ANN""s, which uses control and heuristic search based on position and current feedback, to constantly update the weights of NN in accordance with the parameter variations. Since NN can be trained based on the experimental data obtained from the self-tuning setup which includes the effect of saturation, it may have a very good accuracy. In addition, it offers a good dynamic response and has an excellent self-learning capability. Experimental results are provided which demonstrate an exemplary operation of the self-tuning controller.
Another embodiment of the method according to the present invention provides an ANN based control method for maximizing torque per ampere figure of merit presented. This embodiment is highly accurate, robust and has a good dynamic response, and combines the ANN based control and a procedure of a periodic search for an optimal turn-off angle based on the position of the motor and the current feedback. This method has a self-learning capability as it tunes the weights of the ANN according to parameter variations that affect the inductance profile. This method can be implemented digitally using, e.g., a digital signal processor. This method can be made flexible to optimize new performance index on-line.
Yet another embodiment of the method according to the present invention combines the adaptive ANN based control with a heuristic search method which periodically updates the weights of the NN in accordance with the parameter variations. The NN is trained with the experimental data obtained from the heuristic search based self-tuning setup. Hence it may include the effect of saturation and has a very good accuracy. In addition, it offers a good dynamic response and has an excellent self-learning capability. The dynamic model of the SRM uses static magnetization data generated experimentally. The operation of the SRM from zero speed to the base speed can be considered.
An on-line self-tuning control is preferable to optimize the performance of the SRM drive in the presence of variable variations, and provides the advanced adaptive NN based control to maximize torque per ampere in the low speed region. The method according to the present invention utilizes a heuristic search method to find the change in the optimal excitation instances in case of variable variations. Based on the results of such heuristic search, NN may utilize an incremental learning to adapt its network weights.
Another embodiment of the system and method according to the present invention provides a neural network based torque control method and system for controlling SRM to minimize torque pulsation with a maximum possible torque per ampere figure of merit. Depending on the torque demand, rotor position and rotor speed, the ANN generates optimal current profile to achieve the task. Unlike the other conventional NN based control schemes which use static test data for training, the NN based torque control system and method uses training data generated from a dynamic SRM model. Hence, the system and method control torque on an instantaneous basis rather than on an average basis, allowing an effective torque control with a maximum torque per ampere even during the transient operation of the motor. The effect of magnetic non-linearity in the dynamic SRM model may be taken into consideration by using, e.g., a static magnetization curve. The SRM operation from zero speed to the base speed can also be taken into consideration.